DEV Community: Diana Wachenje

A Beginner's Guide on Data Modelling and Joins

Diana Wachenje — Sat, 26 Sep 2026 21:48:13 +0000

Introduction

When working with data, the data is usually stored in different tables. A good example, a car sales business could have one table containing car information, another containing car information and another containing sales transaction.
Therefore, data modelling, relationships, and joins help us join the data together to make it easy to understand and analyze it.
A good data modelling will ensure that information is well organized correctly, reports and calculations are done for accurate results.

Learning Objectives

By the end of this article, you should be able to understand the following:

. Understand what data modelling means.
. Explain why data modelling is important.
. Understand relationship between tables.
. Explain the different types of relationships.
. Understand what joins are.
. Identify common types of joins.
. Understand how relationships and joins are used in data analysis.

What is Data Modelling?

Data modelling is the process of organizing data into table, define how they are connected.
Instead of putting all the information into one large table, we separate the data into different tables based on what it represents.

For example, a car business may have:

Example of a Customer Table;

A table contains three columns: Customer ID, Customer Name, and Region. I added two customers to the table. The first customer has the ID C001, the name David, and is from Nairobi. The second customer has the ID C002, the name Clara, and is from Mombasa.

Example of a Car Table;

The table contains three columns: Car ID, Car Model, and Brand. I added two cars to the table. The first car has the ID CAR01, the model Fortuner, and the brand Toyota. The second car has the ID CAR02, the third car has the ID CAR03 model CX5, and the brand Mazda.

Example of Sales Table;

The table contains Order ID, Customer ID, Car ID, and Sales Amount. I added two sales records. The first order, 0001, belongs to customer C001 and has a sales amount of 8,500,000. The second order, 0002, belongs to customer C002 and has a sales amount of 4,500,000.

These tables are connected using fields like the Customer ID and Car ID.

1. Why is Data Modelling Important?

A well-designed data model is important because it helps us:

Organize data
It keeps related information in appropriate tables instead of putting everything into one large table.

Reduce Duplication
The same customer does not need to be repeated in every sales record.

Improve reporting
A good model makes it easier to create accurate reports and dashboards.

Support calculations
In tools like Power BI, a good data model helps DAX calculations work correctly.

Improve Performance
Well-organized tables and relationships will help one to report process data more efficiently.

Make the model easier to maintain
When data is well organized properly, it is easier to update, clean and manage.

2. What is a Relationship?

A relationship is a connection between two tables.
For example, the Customer ID in the Customer table can be connected to the Customer in the Sales Table. The Customer table may contain one record for each customer, while the Sales table can contain many sales made by the same customer.

This creates a one-to-many relationship.

For example:

Customer

C001 → David

Sales

C001 → Order 001
C001 → Order 005
C001 → Order 010

One customer can therefore have many sales records.

3. Types of Relationships

There are several types of common types of relationships.

One-to-One (1:1)
One record in one table matches one record in another table.

Example:
One person has one passport record.

One-to-Many (1:M)
One record in one table can match many records in another table.

Example:
One customer can make many orders.
This is one of the most common relationships in data modelling.

Many-to-Many (M:M)
Many records in one table can be related to many records in another table.
For example, one student can take many courses, and one course can have many students.
Many-to-many relationships need to be handled carefully because they can sometimes cause incorrect calculations or duplicate results.

4. Primary Key and Foreign Key

Keys are important when creating relationships.

Primary Key
A primary key is a column that uniquely identifies each record in a table.

For example;

Here, Customer ID can be the primary key because every customer has a unique ID. The Customer table contains three customers identified by unique Customer IDs. Customer C001 is David, Customer C002 is Clara, and Customer C003 is Kevin.

Foreign Key

A foreign key is a column that refers to the primary key of another table.
For example, the Sales table may contain Customer ID:

Order 0001 belongs to Customer C001 and has a Sales Amount of 8,500,000, while Order 0002 belongs to Customer C002 and has a Sales Amount of 4,500,000.

Here, Customer ID is a foreign key because it connects the Sales table to the Customer table.

5. What is a Join?

A join is a way of combining information from two or more tables using a common column.
For example, suppose we have a Customer table and a Sales table.
The Customer table tells us who the customer is, while the Sales table tells us what they purchased.

A join can combine these pieces of information.
For example:

Customer C001 is David and is associated with Order ID 0001, with a Sales Amount of 8,500,000. Customer C002 is Clara and is associated with Order ID 0002, with a Sales Amount of 4,500,000.

The tables have been joined using Customer ID.

6. Common Types of Joins

Inner Join
An inner join returns only records that have matching values in both tables.
For example, if a customer exists in both tables, that customer's information will be included.

Left Join
A left join returns all records from the left table and matching records from the right table. If there is no match, the information from the right table will normally appear as null.

Right Join
A right join returns all records from the right table and matching records from the left table.

Full Outer Join
A full outer join returns all records from both tables.
Records are included whether or not they have a matching value in the other table.

7. Relationships vs Joins

Relationships and joins are related, but they are not exactly the same.
A relationship defines how tables are connected in a data model.
A join combines data from tables based on a matching column.

For example:
In Power BI, relationships are commonly used to connect tables in a data model.
In SQL, joins are commonly used to combine data from different tables.
In Power Query, merge operations can be used to join tables.

8. Example Using a Car Dataset

Imagine you have a car sales dataset with three tables:

Customers

. Customer ID
. Customer Name
. Customer Age
. Region

Cars

. Car ID
. Car Model
. Brand
. Selling Price

Orders

. Order ID
. Customer ID
. Car ID
. Order Date
. Units Sold
The tables can be connected like this:

Customers → Orders ← Cars

The Customer ID connects Customers to Orders, while Car ID connects Cars to Orders.

This model allows you to answer questions such as:
a). How many cars did each customer buy?
b). Which car brand generated the most sales?
c). Which region had the highest sales?
d). How much revenue was generated each month?

Data Modelling in Power BI
In Power BI, data modelling is especially important when creating reports.
You can load different tables into Power BI and create relationships between them. A common structure is a star schema.

A star schema normally has:
. Fact table – contains transactions or measurements, such as sales.
. Dimension tables – contain descriptive information, such as customers, cars, products, or dates.

For example:
Customer Dimension → Sales Fact ← Car Dimension

This structure makes the data easier to analyze and can support efficient DAX calculations.

Conclusion

Data modelling is an important part of data analysis. It helps organize information into tables and shows how those tables are connected.
Relationships connect tables, while joins are used to combine data from different tables.
Understanding primary keys, foreign keys, relationship types, and joins is important for anyone learning SQL, Power BI, Power Query, or data analytics.

A well-designed data model makes reporting, analysis, calculations, and data management easier and more reliable.

Understanding Parametric and Non-Parametric Tests Made Simple

Diana Wachenje — Tue, 07 Jul 2026 20:52:15 +0000

Introduction

Parametric and non-parametric statistical tests are used to analyze data. The main difference is whether the test made assumes the data follow a particular distribution usually known as normal distribution or not.

Definition

1). Parametric tests
Parametric tests are statistical tests used to analyze numerical data and also make assumptions about the population on data that has been collected.

An example: Suppose a researcher wants to know whether a new drug lowers blood pressure more effectively than an old drug.

The blood pressure readings (121,128,135,140 mmHg). These are continuous numerical data. The readings are normally distributed by the researcher through a parametric test known as the independent test to compare the average pressure between the two groups.

2). Non-parametric tests
Non-parametric tests are statistical tests that do not require to follow a specific distribution such as normal distribution. They are used when assumptions for parametric tests are not met.
Example: A doctor compares patients' pain ratings before and after receiving physiotherapy.

Differences between parametric and non-parametric tests.

1). Parametric tests compare means (averages) whereas non-parametric tests compare median, ranks, or frequencies.

2). Parametric tests are more powerful when assumptions are met whereas non-parametric tests are powerful but more robust when assumptions are violated.

3). Parametric tests are more sensitive to extreme outliers whereas non-parametric are less sensitive to outliers because many tests use rank instead raw values.

4). Parametric tests often perform best with moderate or large samples whereas non-parametric are well suited with small samples.

5). Parametric tests assume that the data follows a specific distribution (normal) whereas non-parametric tests do not require the data to follow a specific distribution.

Use of parametric and non-parametric tests and their role in data science.

1). Use of Parametric Tests in Data Science

a). Hypothesis testing.
b). Predictive analytics.
c). Measuring relationships among variables.
d). Evaluating machine learning models using statical comparisons.

2). Uses of Non-Parametric Tests in Data Science

a). Analyzing survey and questionnaire data.
b). Working with non-normal or incomplete datasets.
c). Detecting associations in categorical variables.
d). Validating results when parameters assumptions are not met.

In conclusion parametric and non-parametric tests enable data scientists to choose appropriate analytical methods for different types of data leading to more reliable concluding and better-informed decisions.

STATISTICS MATTERS IN DATA SCIENCE

Diana Wachenje — Wed, 24 Jun 2026 22:18:34 +0000

Introduction

Statistics is a branch of applied mathematics that helps one to collect, organize, analyze, interpret and present data.
Its primary goal is to extract meaningful insights from numerical facts and figures to understand trends, summarize information and make information and make informed decisions in the face of uncertainty.

Statistical and non-statistical analysis

This can be done in two ways;

1). Statistical analysis
This is used to collect, explore and present large amounts of data to identify patterns and trends. It is also called quantitative analysis.
2). Non statistical analysis
It provides generic information and includes text, sound, still images and moving images. It is also called qualitative analysis.

Major categories of statistics

1). Descriptive statistics
It helps organize data and focuses on the main characteristics of the data. It also provides a summary of the data numerically or graphically.
. Examples of descriptive statistics;
. Mean (average)
. Median
. Mode
. Range
. Standard deviation
An example; Finding the average score of students in a class.

2). Inferential statistics
It is used to make predictions and conclusions about a population based on a sample. It also helps in decision and forecasting.
. Examples of inferential statistics;
. Probability testing
. Regression analysis
. Hypothesis making
. Confidence intervals
An example in a polling station a sample of 2000 voters to predict the outcome of a national election.

Types of Data

What is data?
This is a collection of raw fact values measurements collected for analysis. The main types of data are;

1). Qualitative Data (Categorical Data)
This is the type of data that describes qualities or characteristics and cannot be measured numerically.

 Types of Qualitative Data

. Nominal Data
This is data grouped into categories with no specific order.
. Examples of nominal data;
. Gender
. Religion
. Eye color
. Ordinal Data
This is data grouped into categories that have an order or ranking.
. Examples of ordinal data;
. Customer satisfaction (poor, good, excellent)
. Class positions
. Education level

2). Quantitative Data (Numerical Data)
This is the type of data that consists of numbers and can be measured or counted.

Types of Quantitative Data

    . Discrete Data

These are countable numbers, usually whole numbers.
. Examples of discrete data;
. Number of students
. Number of cars
. Goal scored
. Continuous Data
This is data that has measurable values which take any value within a range.
. Examples of continuous data;
. Height
. Weight
. Temperature

Statistical Data analysis steps

1). Define the problem
2). Collect data
3). Organize and clean the data
4). Explore the data
5). Choose the appropriate statistical method
6). Analyze the data
7). Interpret Results
8). Draw conclusions and make decisions
9). Present the findings

Common mistakes

1). Using poor quality data
2). Confusing correlation with causation
3). Misinterpreting Probability
4). Data leaking
5). Poor data visualization

Conclusion
Without statistics, data scientists would find it hard to analyze data science models which will lead to lack of accuracy, reliability, and scientific validity.

# A BEGINNER'S JOURNEY INTO PANDAS

Diana Wachenje — Sun, 21 Jun 2026 21:33:28 +0000

Introduction

Pandas is an open-source library used in python for data cleaning, manipulation, analysis and visualization. It allows users to work with structured data that is tables, spreadsheets and databases efficiently.
The name Pandas came from "Panel Data". It is a term used in statistics and economics.

Importance of Pandas

Organizations produce huge amounts of data every day. Pandas helps professionals to;

. Clean, messy data
. Analyze trends and patterns
. Calculate statistics
. Merge datasets
. Prepare data for machine learning and visualization

Main Data Structures in Pandas

1). Series
A series is like a column in a table. It is a one-dimensional array holding data of any type.

Example:

Python

import pandas as pd
age=pd. Series ([15, 25, 35,40])
print(ages)

Output
Plain text
0 15
1 25
2 35
3 40

2). Data Frame
A Data Frame is a two-dimensional data structure, like two-dimensional array, a table with rows and columns.

Example:

Python

import pandas as pd

lecturers: {

"Name": ["Harun", "Bridgit", "Navas", "Emmanuel"]
"Age": [30,24, 22, 25]
}
df=pd.DataFrame(lecturers)
print(df)

Output

Plain text
Name Age
0 Harun 30
1 Bridgit 24
2 Navas 22
3 Emmanuel 25

Real-Life Uses of Pandas

1). Education
Colleges and schools are able to analyze student performances.

Example:
Python
top_students=df[df["score"]>=80]
print(top_students)

2). Data Science and Machine Learning
Data Scientists are able to clean data, analyze and prepare the data before building new models.

Example:

Python
df.dropna(inplace=True)
This removes missing values from the dataset.

3). Healthcare
Hospitals are able to analyze the number of patients records and also improve the services.

Example:
Python
average_age=patients["age"]mean()
print(average_age)

Healthcare professionals are able to study diseases patterns and patient demographics.

4). Business and Sales Analysis
Companies are able to analyze sales performance and make decisions.

Example:
Python
sales=pd.read_csv("sales.csv")
total_sales=sales["amount"].sum()
print(total_sales)

This helps business to determine:
. Best-selling products
. Total revenue
. Monthly sales trends

Common Errors in Pandas

While working with Pandas we often make these mistakes;

1). Forgetting to import pandas
Python
df=pd.read_csv("data.csv")
Name error: Name 'pd' is not defined

2). Forgetting Parenthesis with Methods
3). Confusing loc and iloc
. Loc uses labels (names)
. Iloc uses positions(numbers)

# Conclusion

Pandas is one of the most powerful libraries in Python with data.
It is commonly used in healthcare, finance, business, education and data science to clean, analyze and interpret data for better decision making. It is very essential for someone learning data science and data analysis.

How to Publish a Power BI Report and Embed it into a website

Diana Wachenje — Sun, 05 Apr 2026 07:00:50 +0000

A brief introduction of Power BI and Publishing Process

Power BI is a business tool that has been developed for analysis by Microsoft to allow user visualization of data, create interactive reports and share some insights across an organization or company. Whereas publishing process is just sharing your report so that others can have access to it. So far learnt about Power BI Queries, DAX, data modelling, joins, charts, dashboards and reporting.

Power BI consists of three main components:

Power BI Desktop - It is used to build reports and data models.
Power BI Service - This is a cloud-based platform which is used to share, collaborate and one is able to access reports online.
Power BI Mobile - These are just apps in the smartphones or tablets used for viewing dashboards.

Here are few steps to follow while creating workspace

1). Create a Report

Ensure you have built your report in Power BI Desktop by using visuals like charts, tables and maps.

2). Save the File

Always ensure your work is saved in (pbix format).

3). Sign In

Log into your Power BI account (Usually through an organization or Microsoft Account). Examples: datascience@luxdevhq.com or johndoe@gmail.com

4). Publish the Report

Click the publish button, a shade of blue at the far end on the left written Publish.
Choose a destination Workspace in Power BI Service this can either be Shared Workspace for an organization or My Workspace for personal use.

5). Access Power BI Service

Your report will be uploaded on cloud where it can be viewed in a browser.

6). Share and Collaborate

Once the report is published you can share with colleagues via links, apps or workspace access.
You can also restrict your work by choosing to make it public or private, choosing who can or cannot see it.

7). Schedule Refresh

This is optional, you can decide to refresh your report so that it can be up to date.

UNDERSTANDING DATA MODELLING IN POWER BI

Diana Wachenje — Mon, 30 Mar 2026 22:04:58 +0000

Introduction

Power BI is a business analytic platform that transforms raw data into actionable insights through visualizations and reports that helps users to analyze data, identify trends securely across organizations to make data-driven decisions.
Data modelling is the process to connect, structure, optimize data tables to work efficiently together.

Concepts to Understand

Joins
Joins are performed in Power Query where you combine two columns by selecting the first and the second table and join them by clicking on the headers. Also ensure they have same data type for accurate matching.
Relationships
Relationships define how table connect. These are,
One-to-Many (Most common)
Many-to-One
Many-to- Many (Used carefully)
Example:
One customer- Many Sales
Schemas
Schemas refers to the way your data is structured either in facts tables or dimensions and organized in the data model.
One Central fact table
Multiple surrounding dimension tables

Power BI Relationships

Cardinality Cardinality is how rows relate:
One-to- One
One-to- Many
Many-to- Many
Cross Filter Direction
It controls how filters move between tables:
Single direction
Both directions

Commonly modelling issues

Not using proper Data Table
Mixing fact and dimensional data
Creating too many many-to-many relationships

Steps-by-steps Data modeling process

Step 1: Load Data
Import from Excel, SQL etc.
Step 2: Clean Data (Power Query)
Fix structure before modelling
Step 3: Create Relationships
Go to model view and connect tables
Step 4: Build Star Schema
Organize tables properly
Step 5: Create measures (DAX)
Add business logic
Step 6: Optimize Model

Remove unused columns
Use correct data types
Avoid unnecessary relationships

# Simple Real-life Examples

In a mini mart setup:

Sales table- transactions
Product table- item details
Customer table- buyer information

## Why Data Modelling is important

Faster reports
Accurate Insights -Easier Dashboards

HOW EXCEL IS USED IN REAL -WORLD ANALYSIS

Diana Wachenje — Sun, 29 Mar 2026 18:28:58 +0000

Microsoft Excel

Microsoft Excel is a spreadsheet that has been designed for data entry methods, basic formula creation, data sorting and filtering, basic charts and visualizations and file management. Through Excel one gets to identify the knowledge level, explore specific features of interests and applies it through practical examples.

How Excel is used in Real World Scenarios

Personal and project management Excel is used for personal finances an individual tracks their budgets, calculate expenses and visualize spending trends with charts. Whereas students; it helps them analyze academic performance, for researching data and organizing course schedules.
Business and Financial Application Excel is used widely in business for organizing data, creating budget forecasts, analyze revenue trends, manage cash flows data analysis and reporting, operational and resource management, administrative and scheduling, inventory and pharmacy management, financial and billing, patient monitoring and research to track the performance.

Examples of features and formulas

Excel has different functions which are used in the worksheet to perform calculations in the cell values provided.

Basic Functions

SUM, AVERAGE, COUNT, MAX, MIN
Mathematical operations
Text manipulation functions
Date and time functions

Intermediate Functions

VLOOKUP, HLOOKUP, INDEX, MATCH -Logical functions (IF, AND, OR, NOT) -Error handling (IFERROR, IFNA)
COUNTIF, SUMIF and the variants

Advanced Functions

Array formulas
Financial functions (NPV, PMT)
Statical analysis function
Database functions
Power Query introduction

# Data analysis Features

PivotTables and Pivot Charts
Data validation techniques
Conditional formatting strategies
What-if analysis tools
Dashboard creation techniques